Syllabus
1. Introduction, Quantitative Methods in Political Science (February 23)
Students will be introduced to the content of the course. The research philosophy of quantitative approaches to science and the specifics of these approaches will be discussed, especially with regard to qualitative methods. The basic concepts of quantitative methods, probability theory and statistics will be repeated.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Los Angeles: Sage. Chapter 1 – Why is my evil lecturer forcing me to learn statistics?
Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge University Press. Chapter 17 – Data Management. 2. Working in RStudio (March 9)
The R programming environment will be introduced, especially the individual types of information used; commands for saving and uploading data files, their sorting, editing and basic descriptive analysis; work with variables; creation of functions. Students will be introduced to the main advantages of R over other programs, as well as the pitfalls that may occur when working with this program.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Sage. Chapter 3 – The R environment.
Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge University Press. Chapter 18 – Univariate Statistics. 3. Data Visualization (March 23)
Students will be introduced to different ways of visualizing data and outputs of quantitative analyzes. Ways of data analysis will be revealed through their visualization. Students will be acquainted with the mistakes that should not be committed in the data visualization. The creation of these visualizations will be practically practiced in R.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 4 – Exploring data with graphs.
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 19 – Probability Distributions. 4. Statistical Inference and Basic Instruments (April 6)
The logic of statistical inference will be described with emphasis on meeting the relevant assumptions. Students will get acquainted with the principle of the central limit theorem and acquire the skills of constructing confidence intervals in R. Furthermore, correlation analysis will be presented, especially through situations suitable for its use, the specific form of application and the method of interpretation of results. Students will practically try statistical reasoning and basic analysis in the programming environment R (correlation analysis, t-test).
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 2 – Everything You Ever Wanted to Know About Statistics (Well, Sort of).
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 20 – Statistical Inference.
Gerring, J., & Christenson, D. (2017). Applied social science methodology: an introductory guide. Cambridge University Press. Chapter 21 – Bivariate Statistics. 5. Regression Analysis (April 20)
The method of regression analysis, which is the leading quantitative method in political science research, will be characterized. Emphasis will be placed on the assumptions of regression analysis, the analysis itself and the interpretation of its results. Students will practically try the application of regression analysis and presentation of results in tabular and graphical form in R. Students will be introduced to the technique of data transformation so that the classic regression analysis can be used.
Reading:
Field, A. P., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage. Chapter 7 – Regression.
Gerring, J., & Christenson, D. (2017). Applied Social Science Methodology: An Introductory Guide. Cambridge University Press. Chapter 22 – Regression. 6. Mapping (May 4)
Maps are becoming increasingly important in data analysis. Students will therefore get acquainted with the role of spatial analysis in the field of political science research. At the same time, they will practically create map creation directly within RStudio.
Reading:
Lysek, J., Pánek, J., & Lebeda, T. (2020). Who are the voters and where are they? Using spatial statistics to analyse voting patterns in the parliamentary elections of the Czech Republic. Journal of Maps, 17(1), 33–38.
Annotation
The course acquaints students with advanced tools in the field of data analysis and quantitative political science research, both at the theoretical and especially practical levels. Given that quantitative methods are becoming increasingly important in contemporary political science, their knowledge is a very valuable and essentially necessary skill.
After completing the seminar, students will be able to work passively with existing research based on quantitative methods and critically evaluate the results of such research. At the same time, they will be sufficiently experienced to actively use the basic statistical tools that are most used in modern political science. In addition, knowledge of the R programming environment will open up other possibilities for students to work with data and, in general, will certainly increase their opportunities for employment.